Generalizing CNNs to inputs with graph-structured data is an important topic in the field of deep learning. Advances in this direction are often categorized as spectral-based approaches and spatial-based approaches. Spectral-based approaches work with a spectral representation of the graphs and have been successfully applied in the context of node classification. The first prominent spectral research on spectral Graph Convolutional Network (GCN) is presented in [1]}. Later, subsequent studies aim to designing more efficient and flexible spectral-based solutions [2]} [3]} [4]}. In all of the aforementioned spectral approaches, the learned filters depend on the Laplacian eigenbasis. Spatial-based approaches define graph convolutions based on a node's spatial relations, where an image can be considered as a special form of a graph with each pixel representing a node [5]}. Graph Attention Network (GAT) [6]} is a representative approach toward spatial-based methods. It introduces an attention-based architecture to perform node classification of graph-structured data, which computes the hidden representations of each node in the graph by attending over its neighbors.
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